Carreau Akışkanının Dikey Germe Silindirindeki Akış Karakteristiklerinin Yapay Zeka Yaklaşımıyla Analizi

Year 2023, Volume: 64 Issue: 710, 91 - 113, 04.04.2023

Andaç Batur Çolak

https://doi.org/10.46399/muhendismakina.1165731

Abstract

Bu çalışmada, Carreau akışkan akışının gözenekli bir ortama daldırılmış dikey bir germe silindiri üzerindeki akış karakteristikleri, yapay zeka yaklaşımı ile detaylı olarak analiz edilmiştir. Akış parametreleri olarak lokal yüzey sürtünmesi, lokal Nusselt sayısı ve lokal Sherwood sayısı parametreleri ele alınmıştır. Akış parametrelerini tahmin etmek için çok katmanlı algılayıcı mimarisine sahip üç farklı yapay sinir ağı modeli tasarlanmıştır. Literatürden elde edilmiş nümerik veri seti kullanılarak eğitilmiş olan ağ modellerinde Bayesian Düzenlileştirme eğitim algoritması kullanılmıştır. Farklı performans parametreleri dikkate alınarak optimize edilen yapay sinir ağlarında tahmin performansı en yüksek olan modeller tercih edilmiştir. Elde edilen tahmini değerler, hedef verilerle karşılaştırılmıştır. Ayrıca performans parametreleri de hesaplanmış ve ağ modellerinin performansları kapsamlı bir şekilde analiz edilmiştir. Çalışma bulguları, geliştirilmiş olan yapay sinir ağlarının, doğal taşınımlı Carreau akışına ait parametreleri yüksek doğrulukta tahmin edebildiğini ortaya koymuştur.

Keywords

Carreau akışkanı, germe silindiri, akış karakteristikleri, doğal taşınım, yapay sinir ağı

Thanks

Bu çalışmada kullanılan veri seti, Lim ve diğ. (2022) tarafından yapılmış olan çalışmadan elde edilmiştir. Yazar, katkılarından dolayı Lim ve diğ. (2022)’ne teşekkür eder.

Analysis of Flow Characteristics of Carreau Fluid in a Vertical Stretching Cylinder with Artificial Intelligence Approach

Abstract

In this study, the flow characteristics of Carreau fluid flow on a vertical stretching cylinder immersed in a porous medium were analyzed in detail with an artificial intelligence approach. Local skin friction, local Nusselt number and local Sherwood number parameters are considered as flow parameters. Three different neural network models with multilayer perceptron architecture are designed to estimate the flow parameters. Bayesian Regularization training algorithm was used in artificial neural networks trained using a numerically calculated data set obtained from the literature. The models with the highest prediction performance were preferred in the artificial neural networks optimized by considering different performance parameters. The estimated values obtained were compared with the target data. In addition, the performance parameters were calculated and the performances of the network models were analyzed comprehensively. The study findings showed that neural networks are an ideal artificial intelligence tool that can be used to predict the flow properties of natural convection Carreau fluid flow towards the stretching cylinder.

Keywords

Carreau fluid, stretching cylinder, flow characteristics, natural convection, artificial neural network

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